# import os
#
# import torch
#
# from modules import shared, paths, sd_disable_initialization, devices
#
# sd_configs_path = shared.sd_configs_path
# # sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
# # sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference")
#
#
# config_default = shared.sd_default_config
# # config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
# config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
# config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
# config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
# config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
# config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml")
# config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
# config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
# config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
# config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
# config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
# config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
# config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
# config_sd3 = os.path.join(sd_configs_path, "sd3-inference.yaml")
#
#
# def is_using_v_parameterization_for_sd2(state_dict):
#     """
#     Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
#     """
#
#     import ldm.modules.diffusionmodules.openaimodel
#
#     device = devices.device
#
#     with sd_disable_initialization.DisableInitialization():
#         unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
#             use_checkpoint=False,
#             use_fp16=False,
#             image_size=32,
#             in_channels=4,
#             out_channels=4,
#             model_channels=320,
#             attention_resolutions=[4, 2, 1],
#             num_res_blocks=2,
#             channel_mult=[1, 2, 4, 4],
#             num_head_channels=64,
#             use_spatial_transformer=True,
#             use_linear_in_transformer=True,
#             transformer_depth=1,
#             context_dim=1024,
#             legacy=False
#         )
#         unet.eval()
#
#     with torch.no_grad():
#         unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
#         unet.load_state_dict(unet_sd, strict=True)
#         unet.to(device=device, dtype=devices.dtype_unet)
#
#         test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
#         x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
#
#         with devices.autocast():
#             out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().cpu().item()
#
#     return out < -1
#
#
# def guess_model_config_from_state_dict(sd, filename):
#     sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
#     diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
#     sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
#
#     if "model.diffusion_model.x_embedder.proj.weight" in sd:
#         return config_sd3
#
#     if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
#         if diffusion_model_input.shape[1] == 9:
#             return config_sdxl_inpainting
#         else:
#             return config_sdxl
#
#     if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
#         return config_sdxl_refiner
#     elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
#         return config_depth_model
#     elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
#         return config_unclip
#     elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
#         return config_unopenclip
#
#     if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
#         if diffusion_model_input.shape[1] == 9:
#             return config_sd2_inpainting
#         # elif is_using_v_parameterization_for_sd2(sd):
#         #     return config_sd2v
#         else:
#             return config_sd2v
#
#     if diffusion_model_input is not None:
#         if diffusion_model_input.shape[1] == 9:
#             return config_inpainting
#         if diffusion_model_input.shape[1] == 8:
#             return config_instruct_pix2pix
#
#     if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
#         if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
#             return config_alt_diffusion_m18
#         return config_alt_diffusion
#
#     return config_default
#
#
# def find_checkpoint_config(state_dict, info):
#     if info is None:
#         return guess_model_config_from_state_dict(state_dict, "")
#
#     config = find_checkpoint_config_near_filename(info)
#     if config is not None:
#         return config
#
#     return guess_model_config_from_state_dict(state_dict, info.filename)
#
#
# def find_checkpoint_config_near_filename(info):
#     if info is None:
#         return None
#
#     config = f"{os.path.splitext(info.filename)[0]}.yaml"
#     if os.path.exists(config):
#         return config
#
#     return None
#
